Development of Safety Monitoring System of Connected and Automated Vehicles considering the Trade-off between Communication Efficiency and Data Reliability

The safety of urban transportation systems is considered a public health issue worldwide, and many researchers have contributed to improving it. Connected automated vehicles (CAVs) and cooperative intelligent transportation systems (C-ITSs) are considered solutions to ensure urban transportation systems' safety using various sensors and communication devices. However, it is found difficult to deploy the C-ITS framework in South Korea, because CAVs produce a massive amount of data every minute but it cannot be transmitted via existing communication network. Thus, raw data must be sampled to reduce the size of the data over communication network and transmitted to the server for further processing. On the other hand, the sampled data must be highly accurate to ensure the safety of different agents in C-ITS. Thus, in this study, we designed and developed a C-ITS architecture and data flow, including messages and protocols for the safety monitoring system of CAVs, and determined the optimal sampling interval for data transmission while considering the trade-off between communication efficiency and reliability of safety performance indicators. Three safety performance indicators were introduced: severe deceleration, lateral position variance, and inverse time to collision. A field test is conducted to collect data from various sensors installed in the CAV, determining the optimal sampling interval. Kolmogorov-Smirnov test is conducted to ensure statistical consistency between sampled and raw datasets. The effects of the sampling interval on message delay, data accuracy, and communication efficiency were analyzed.